Robotic artificial muscles are a subset of artificial muscles that are capable of producing biologically inspired motions useful for robot systems, i.e., large power-to-weight ratios, inherent compliance, and large range of motions. These actuators, ranging from shape memory alloys to dielectric elastomers, are increasingly popular for biomimetic robots as they may operate without using complex linkage designs or other cumbersome mechanisms. Recent achievements in fabrication, modeling, and control methods have significantly contributed to their potential utilization in a wide range of applications. However, no survey paper has gone into depth regarding considerations pertaining to their selection, design, and usage in generating biomimetic motions. In this paper, we discuss important characteristics and considerations in the selection, design, and implementation of various prominent and unique robotic artificial muscles for biomimetic robots, and provide perspectives on next-generation muscle-powered robots.

Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force generation, small actuation range, and poor compactness to name a few. Despite advances in this area, realizing compact soft grippers, which exhibit high dexterity and force output, is still challenging. This article explores using twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been widely used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by the human hand, with four fingers and a thumb. Tunable stiffness was implemented in the fingers by using antagonistic TSAs. The fingers' bending angles, actuation speed, blocked force output, and stiffness tuning are experimentally characterized. The gripper achieves a score of 6 on the Kapandji test and recreate 31 of the 33 grasps of the Feix GRASP taxonomy. It exhibits a maximum grasping force of 72 N, which is almost 13 times its own weight. A comparison study reveals that the proposed gripper exhibits equivalent or superior performance compared to other similar soft grippers.

Super-coiled polymer (SCP) artificial muscles have many attractive properties, such as high energy density, large contractions, and good dynamic range. To fully utilize them for robotic applications, it is necessary to determine how to scale them up effectively. Bundling of SCP actuators, as though they are individual threads in woven textiles, can demonstrate the versatility of SCP actuators and artificial muscles in general. However, this versatility comes with a need to understand how different bundling techniques can be achieved with these actuators and how they may trade off in performance. This letter presents the first quantitative comparison, analysis, and modeling of bundled SCP actuators. By exploiting weaving and braiding techniques, three new types of bundled SCP actuators are created: woven bundles, two-dimensional, and three-dimensional braided bundles. The bundle performance is adjustable by employing different numbers of individual actuators. Experiments are conducted to characterize and compare the force, strain, and speed of different bundles, and a linear model is proposed to predict their performance. This work lays the foundation for model-based SCP-actuated textiles, and physically scaling robots that employ SCP actuators as the driving mechanism.

The nature-inspired behavior of collective motion is found to be an optimal solution in swarming systems for predator avoidance and survival. In this work, we propose a two-level control architecture for multi-robot systems (MRS), which leverages the advantages of flocking control and function approximated reinforcement learning for predator avoidance task. Reinforcement learning in multi-agent systems has gained a tremendous amount of interest in recent few years. Computationally intensive architectures such as deep reinforcement learning and actor-critic approaches have been extensively developed and have proved to be extremely efficient. The proposed approach, comprising of cooperative function approximated Q-learning, is applied such that it ensures formation maintenance in MRS while predator avoidance. A consensus filter is incorporated into the control architecture, to sense predators in close vicinity in a distributed and cooperative fashion to ensure consensus on states among the robots in the system. The proposed approach is proved to be convergent and results in superior performance in unexplored states and reduced number of variables. Simulation results confirm the effectiveness of the proposed approach over existing methods. We expect that the proposed approach can be conveniently applied to many other areas with little modifications, such as fire-fighting robots, surveillance and patrolling robots.